Overview

Dataset statistics

Number of variables28
Number of observations5043
Missing cells2405
Missing cells (%)1.7%
Duplicate rows45
Duplicate rows (%)0.9%
Total size in memory1.1 MiB
Average record size in memory224.0 B

Variable types

Categorical3
Text9
Numeric16

Alerts

Dataset has 45 (0.9%) duplicate rowsDuplicates
actor_1_fb_likes is highly overall correlated with actor_2_fb_likes and 2 other fieldsHigh correlation
actor_2_fb_likes is highly overall correlated with actor_1_fb_likes and 2 other fieldsHigh correlation
actor_3_fb_likes is highly overall correlated with actor_1_fb_likes and 2 other fieldsHigh correlation
aspect_ratio is highly overall correlated with content_ratingHigh correlation
budget is highly overall correlated with gross and 2 other fieldsHigh correlation
cast_total_fb_likes is highly overall correlated with actor_1_fb_likes and 2 other fieldsHigh correlation
content_rating is highly overall correlated with aspect_ratioHigh correlation
gross is highly overall correlated with budget and 2 other fieldsHigh correlation
language is highly overall correlated with budgetHigh correlation
num_critic_for_reviews is highly overall correlated with num_user_for_reviews and 1 other fieldsHigh correlation
num_user_for_reviews is highly overall correlated with gross and 2 other fieldsHigh correlation
num_voted_users is highly overall correlated with budget and 3 other fieldsHigh correlation
color is highly imbalanced (75.0%)Imbalance
language is highly imbalanced (88.8%)Imbalance
content_rating is highly imbalanced (50.4%)Imbalance
director_name has 104 (2.1%) missing valuesMissing
director_fb_likes has 104 (2.1%) missing valuesMissing
gross has 677 (13.4%) missing valuesMissing
plot_keywords has 153 (3.0%) missing valuesMissing
content_rating has 303 (6.0%) missing valuesMissing
budget has 406 (8.1%) missing valuesMissing
title_year has 108 (2.1%) missing valuesMissing
aspect_ratio has 329 (6.5%) missing valuesMissing
budget is highly skewed (γ1 = 48.57751667)Skewed
director_fb_likes has 907 (18.0%) zerosZeros
actor_3_fb_likes has 89 (1.8%) zerosZeros
facenumber_in_poster has 2152 (42.7%) zerosZeros
actor_2_fb_likes has 55 (1.1%) zerosZeros
movie_fb_likes has 2181 (43.2%) zerosZeros

Reproduction

Analysis started2024-04-11 09:24:09.834893
Analysis finished2024-04-11 09:24:59.331482
Duration49.5 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

color
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing19
Missing (%)0.4%
Memory size39.5 KiB
Color
4815 
Black and White
 
209

Length

Max length16
Median length5
Mean length5.4576035
Min length5

Characters and Unicode

Total characters27419
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowColor
2nd rowColor
3rd rowColor
4th rowColor
5th rowColor

Common Values

ValueCountFrequency (%)
Color 4815
95.5%
Black and White 209
 
4.1%
(Missing) 19
 
0.4%

Length

2024-04-11T11:24:59.421382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T11:24:59.573382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
color 4815
88.5%
black 209
 
3.8%
and 209
 
3.8%
white 209
 
3.8%

Most occurring characters

ValueCountFrequency (%)
o 9630
35.1%
l 5024
18.3%
C 4815
17.6%
r 4815
17.6%
627
 
2.3%
a 418
 
1.5%
B 209
 
0.8%
c 209
 
0.8%
k 209
 
0.8%
n 209
 
0.8%
Other values (6) 1254
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21559
78.6%
Uppercase Letter 5233
 
19.1%
Space Separator 627
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 9630
44.7%
l 5024
23.3%
r 4815
22.3%
a 418
 
1.9%
c 209
 
1.0%
k 209
 
1.0%
n 209
 
1.0%
d 209
 
1.0%
h 209
 
1.0%
i 209
 
1.0%
Other values (2) 418
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
C 4815
92.0%
B 209
 
4.0%
W 209
 
4.0%
Space Separator
ValueCountFrequency (%)
627
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26792
97.7%
Common 627
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 9630
35.9%
l 5024
18.8%
C 4815
18.0%
r 4815
18.0%
a 418
 
1.6%
B 209
 
0.8%
c 209
 
0.8%
k 209
 
0.8%
n 209
 
0.8%
d 209
 
0.8%
Other values (5) 1045
 
3.9%
Common
ValueCountFrequency (%)
627
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27419
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 9630
35.1%
l 5024
18.3%
C 4815
17.6%
r 4815
17.6%
627
 
2.3%
a 418
 
1.5%
B 209
 
0.8%
c 209
 
0.8%
k 209
 
0.8%
n 209
 
0.8%
Other values (6) 1254
 
4.6%

director_name
Text

MISSING 

Distinct2398
Distinct (%)48.6%
Missing104
Missing (%)2.1%
Memory size39.5 KiB
2024-04-11T11:24:59.921944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length32
Median length24
Mean length13.084835
Min length3

Characters and Unicode

Total characters64626
Distinct characters76
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1504 ?
Unique (%)30.5%

Sample

1st rowJames Cameron
2nd rowGore Verbinski
3rd rowSam Mendes
4th rowChristopher Nolan
5th rowDoug Walker
ValueCountFrequency (%)
john 180
 
1.8%
david 150
 
1.5%
michael 127
 
1.2%
james 87
 
0.8%
peter 85
 
0.8%
robert 84
 
0.8%
paul 81
 
0.8%
richard 80
 
0.8%
scott 65
 
0.6%
lee 58
 
0.6%
Other values (2966) 9277
90.3%
2024-04-11T11:25:00.472949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6097
 
9.4%
5335
 
8.3%
a 5278
 
8.2%
n 4658
 
7.2%
r 4447
 
6.9%
o 3794
 
5.9%
i 3693
 
5.7%
l 2970
 
4.6%
t 2321
 
3.6%
s 2089
 
3.2%
Other values (66) 23944
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48451
75.0%
Uppercase Letter 10493
 
16.2%
Space Separator 5335
 
8.3%
Other Punctuation 260
 
0.4%
Dash Punctuation 87
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6097
12.6%
a 5278
10.9%
n 4658
9.6%
r 4447
 
9.2%
o 3794
 
7.8%
i 3693
 
7.6%
l 2970
 
6.1%
t 2321
 
4.8%
s 2089
 
4.3%
h 1851
 
3.8%
Other values (31) 11253
23.2%
Uppercase Letter
ValueCountFrequency (%)
S 999
 
9.5%
J 925
 
8.8%
M 886
 
8.4%
R 758
 
7.2%
C 712
 
6.8%
B 678
 
6.5%
D 619
 
5.9%
A 569
 
5.4%
L 499
 
4.8%
P 488
 
4.7%
Other values (21) 3360
32.0%
Other Punctuation
ValueCountFrequency (%)
. 239
91.9%
' 21
 
8.1%
Space Separator
ValueCountFrequency (%)
5335
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 87
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 58944
91.2%
Common 5682
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6097
 
10.3%
a 5278
 
9.0%
n 4658
 
7.9%
r 4447
 
7.5%
o 3794
 
6.4%
i 3693
 
6.3%
l 2970
 
5.0%
t 2321
 
3.9%
s 2089
 
3.5%
h 1851
 
3.1%
Other values (62) 21746
36.9%
Common
ValueCountFrequency (%)
5335
93.9%
. 239
 
4.2%
- 87
 
1.5%
' 21
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64484
99.8%
None 142
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6097
 
9.5%
5335
 
8.3%
a 5278
 
8.2%
n 4658
 
7.2%
r 4447
 
6.9%
o 3794
 
5.9%
i 3693
 
5.7%
l 2970
 
4.6%
t 2321
 
3.6%
s 2089
 
3.2%
Other values (46) 23802
36.9%
None
ValueCountFrequency (%)
é 45
31.7%
á 19
13.4%
ö 16
 
11.3%
ó 16
 
11.3%
í 8
 
5.6%
ñ 7
 
4.9%
Ã¥ 6
 
4.2%
ç 5
 
3.5%
É 3
 
2.1%
Ô 2
 
1.4%
Other values (10) 15
 
10.6%

num_critic_for_reviews
Real number (ℝ)

HIGH CORRELATION 

Distinct528
Distinct (%)10.6%
Missing50
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean140.19427
Minimum1
Maximum813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:00.667907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q150
median110
Q3195
95-th percentile387
Maximum813
Range812
Interquartile range (IQR)145

Descriptive statistics

Standard deviation121.60168
Coefficient of variation (CV)0.86737977
Kurtosis2.9134164
Mean140.19427
Median Absolute Deviation (MAD)68
Skewness1.5165327
Sum699990
Variance14786.967
MonotonicityNot monotonic
2024-04-11T11:25:00.872457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 43
 
0.9%
9 37
 
0.7%
5 36
 
0.7%
8 35
 
0.7%
10 35
 
0.7%
12 34
 
0.7%
16 33
 
0.7%
81 33
 
0.7%
43 31
 
0.6%
29 30
 
0.6%
Other values (518) 4646
92.1%
(Missing) 50
 
1.0%
ValueCountFrequency (%)
1 43
0.9%
2 26
0.5%
3 24
0.5%
4 29
0.6%
5 36
0.7%
6 28
0.6%
7 23
0.5%
8 35
0.7%
9 37
0.7%
10 35
0.7%
ValueCountFrequency (%)
813 1
< 0.1%
775 1
< 0.1%
765 1
< 0.1%
750 2
< 0.1%
739 1
< 0.1%
738 1
< 0.1%
733 1
< 0.1%
723 1
< 0.1%
712 1
< 0.1%
703 2
< 0.1%

duration
Real number (ℝ)

Distinct191
Distinct (%)3.8%
Missing15
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean107.20107
Minimum7
Maximum511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:01.063467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile81
Q193
median103
Q3118
95-th percentile146
Maximum511
Range504
Interquartile range (IQR)25

Descriptive statistics

Standard deviation25.197441
Coefficient of variation (CV)0.2350484
Kurtosis22.565797
Mean107.20107
Median Absolute Deviation (MAD)12
Skewness2.339134
Sum539007
Variance634.91102
MonotonicityNot monotonic
2024-04-11T11:25:01.243500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 161
 
3.2%
100 141
 
2.8%
101 139
 
2.8%
98 135
 
2.7%
97 131
 
2.6%
93 129
 
2.6%
95 124
 
2.5%
94 124
 
2.5%
99 124
 
2.5%
96 113
 
2.2%
Other values (181) 3707
73.5%
ValueCountFrequency (%)
7 2
 
< 0.1%
11 1
 
< 0.1%
14 1
 
< 0.1%
20 1
 
< 0.1%
22 7
0.1%
23 2
 
< 0.1%
24 2
 
< 0.1%
25 4
0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
511 1
< 0.1%
334 1
< 0.1%
330 1
< 0.1%
325 1
< 0.1%
300 1
< 0.1%
293 1
< 0.1%
289 1
< 0.1%
286 1
< 0.1%
280 1
< 0.1%
271 1
< 0.1%

director_fb_likes
Real number (ℝ)

MISSING  ZEROS 

Distinct435
Distinct (%)8.8%
Missing104
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean686.50921
Minimum0
Maximum23000
Zeros907
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:01.420499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median49
Q3194.5
95-th percentile973
Maximum23000
Range23000
Interquartile range (IQR)187.5

Descriptive statistics

Standard deviation2813.3286
Coefficient of variation (CV)4.0980202
Kurtosis27.256289
Mean686.50921
Median Absolute Deviation (MAD)49
Skewness5.2297012
Sum3390669
Variance7914817.9
MonotonicityNot monotonic
2024-04-11T11:25:01.607458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 907
 
18.0%
3 70
 
1.4%
6 66
 
1.3%
7 64
 
1.3%
2 63
 
1.2%
4 60
 
1.2%
11 59
 
1.2%
10 53
 
1.1%
8 52
 
1.0%
5 52
 
1.0%
Other values (425) 3493
69.3%
(Missing) 104
 
2.1%
ValueCountFrequency (%)
0 907
18.0%
2 63
 
1.2%
3 70
 
1.4%
4 60
 
1.2%
5 52
 
1.0%
6 66
 
1.3%
7 64
 
1.3%
8 52
 
1.0%
9 49
 
1.0%
10 53
 
1.1%
ValueCountFrequency (%)
23000 1
 
< 0.1%
22000 8
 
0.2%
21000 10
 
0.2%
20000 1
 
< 0.1%
18000 4
 
0.1%
17000 20
0.4%
16000 28
0.6%
15000 2
 
< 0.1%
14000 30
0.6%
13000 26
0.5%

actor_3_fb_likes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct906
Distinct (%)18.0%
Missing23
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean645.00976
Minimum0
Maximum23000
Zeros89
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:01.776455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q1133
median371.5
Q3636
95-th percentile1000
Maximum23000
Range23000
Interquartile range (IQR)503

Descriptive statistics

Standard deviation1665.0417
Coefficient of variation (CV)2.581421
Kurtosis60.563888
Mean645.00976
Median Absolute Deviation (MAD)248.5
Skewness7.2790208
Sum3237949
Variance2772364
MonotonicityNot monotonic
2024-04-11T11:25:01.967497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 126
 
2.5%
0 89
 
1.8%
11000 29
 
0.6%
3 28
 
0.6%
2000 27
 
0.5%
3000 26
 
0.5%
826 22
 
0.4%
2 21
 
0.4%
7 21
 
0.4%
4 21
 
0.4%
Other values (896) 4610
91.4%
(Missing) 23
 
0.5%
ValueCountFrequency (%)
0 89
1.8%
2 21
 
0.4%
3 28
 
0.6%
4 21
 
0.4%
5 18
 
0.4%
6 18
 
0.4%
7 21
 
0.4%
8 17
 
0.3%
9 16
 
0.3%
10 12
 
0.2%
ValueCountFrequency (%)
23000 2
 
< 0.1%
20000 1
 
< 0.1%
19000 5
 
0.1%
17000 1
 
< 0.1%
16000 3
 
0.1%
15000 1
 
< 0.1%
14000 6
 
0.1%
13000 5
 
0.1%
12000 8
 
0.2%
11000 29
0.6%
Distinct3032
Distinct (%)60.3%
Missing13
Missing (%)0.3%
Memory size39.5 KiB
2024-04-11T11:25:02.291493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length13.074354
Min length3

Characters and Unicode

Total characters65764
Distinct characters80
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2089 ?
Unique (%)41.5%

Sample

1st rowJoel David Moore
2nd rowOrlando Bloom
3rd rowRory Kinnear
4th rowChristian Bale
5th rowRob Walker
ValueCountFrequency (%)
michael 102
 
1.0%
david 60
 
0.6%
john 56
 
0.5%
james 53
 
0.5%
scott 52
 
0.5%
tom 50
 
0.5%
jason 44
 
0.4%
robert 44
 
0.4%
kevin 41
 
0.4%
adam 39
 
0.4%
Other values (3825) 9861
94.8%
2024-04-11T11:25:02.831912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6221
 
9.5%
a 5930
 
9.0%
5372
 
8.2%
n 4762
 
7.2%
r 4398
 
6.7%
i 4018
 
6.1%
o 3645
 
5.5%
l 3420
 
5.2%
t 2348
 
3.6%
s 2160
 
3.3%
Other values (70) 23490
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49447
75.2%
Uppercase Letter 10686
 
16.2%
Space Separator 5372
 
8.2%
Other Punctuation 189
 
0.3%
Dash Punctuation 64
 
0.1%
Decimal Number 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6221
12.6%
a 5930
12.0%
n 4762
9.6%
r 4398
8.9%
i 4018
 
8.1%
o 3645
 
7.4%
l 3420
 
6.9%
t 2348
 
4.7%
s 2160
 
4.4%
h 1796
 
3.6%
Other values (38) 10749
21.7%
Uppercase Letter
ValueCountFrequency (%)
M 999
 
9.3%
S 821
 
7.7%
C 815
 
7.6%
B 773
 
7.2%
J 770
 
7.2%
D 668
 
6.3%
A 640
 
6.0%
R 592
 
5.5%
L 511
 
4.8%
T 463
 
4.3%
Other values (16) 3634
34.0%
Other Punctuation
ValueCountFrequency (%)
. 124
65.6%
' 65
34.4%
Decimal Number
ValueCountFrequency (%)
0 3
50.0%
5 3
50.0%
Space Separator
ValueCountFrequency (%)
5372
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 60133
91.4%
Common 5631
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6221
 
10.3%
a 5930
 
9.9%
n 4762
 
7.9%
r 4398
 
7.3%
i 4018
 
6.7%
o 3645
 
6.1%
l 3420
 
5.7%
t 2348
 
3.9%
s 2160
 
3.6%
h 1796
 
3.0%
Other values (64) 21435
35.6%
Common
ValueCountFrequency (%)
5372
95.4%
. 124
 
2.2%
' 65
 
1.2%
- 64
 
1.1%
0 3
 
0.1%
5 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65642
99.8%
None 122
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6221
 
9.5%
a 5930
 
9.0%
5372
 
8.2%
n 4762
 
7.3%
r 4398
 
6.7%
i 4018
 
6.1%
o 3645
 
5.6%
l 3420
 
5.2%
t 2348
 
3.6%
s 2160
 
3.3%
Other values (48) 23368
35.6%
None
ValueCountFrequency (%)
é 43
35.2%
í 14
 
11.5%
á 10
 
8.2%
ë 8
 
6.6%
ó 6
 
4.9%
ø 6
 
4.9%
Ã¥ 5
 
4.1%
ü 4
 
3.3%
ï 3
 
2.5%
ç 3
 
2.5%
Other values (12) 20
16.4%

actor_1_fb_likes
Real number (ℝ)

HIGH CORRELATION 

Distinct878
Distinct (%)17.4%
Missing7
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6560.0471
Minimum0
Maximum640000
Zeros26
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:03.025778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile95.5
Q1614
median988
Q311000
95-th percentile24000
Maximum640000
Range640000
Interquartile range (IQR)10386

Descriptive statistics

Standard deviation15020.759
Coefficient of variation (CV)2.2897334
Kurtosis683.54736
Mean6560.0471
Median Absolute Deviation (MAD)752.5
Skewness19.121776
Sum33036397
Variance2.256232 × 108
MonotonicityNot monotonic
2024-04-11T11:25:03.193811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 449
 
8.9%
11000 211
 
4.2%
2000 197
 
3.9%
3000 155
 
3.1%
12000 135
 
2.7%
13000 127
 
2.5%
14000 123
 
2.4%
10000 112
 
2.2%
18000 109
 
2.2%
22000 82
 
1.6%
Other values (868) 3336
66.2%
ValueCountFrequency (%)
0 26
0.5%
2 8
 
0.2%
3 4
 
0.1%
4 2
 
< 0.1%
5 7
 
0.1%
6 3
 
0.1%
7 3
 
0.1%
8 1
 
< 0.1%
9 3
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
640000 1
 
< 0.1%
260000 3
 
0.1%
164000 2
 
< 0.1%
137000 2
 
< 0.1%
87000 8
 
0.2%
77000 1
 
< 0.1%
49000 27
0.5%
46000 1
 
< 0.1%
45000 5
 
0.1%
44000 2
 
< 0.1%

gross
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4224
Distinct (%)96.7%
Missing677
Missing (%)13.4%
Infinite0
Infinite (%)0.0%
Mean46720941
Minimum162
Maximum7.6050585 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:03.386805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum162
5-th percentile59477.75
Q14587414.5
median24004159
Q359548720
95-th percentile1.7731869 × 108
Maximum7.6050585 × 108
Range7.6050568 × 108
Interquartile range (IQR)54961305

Descriptive statistics

Standard deviation67365553
Coefficient of variation (CV)1.4418707
Kurtosis15.481531
Mean46720941
Median Absolute Deviation (MAD)22300451
Skewness3.1921801
Sum2.0398363 × 1011
Variance4.5381178 × 1015
MonotonicityNot monotonic
2024-04-11T11:25:03.564364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000000 4
 
0.1%
34964818 3
 
0.1%
26410477 3
 
0.1%
8000000 3
 
0.1%
218051260 3
 
0.1%
5773519 3
 
0.1%
7000000 3
 
0.1%
177343675 3
 
0.1%
3000000 3
 
0.1%
144512310 3
 
0.1%
Other values (4214) 4335
86.0%
(Missing) 677
 
13.4%
ValueCountFrequency (%)
162 1
< 0.1%
423 1
< 0.1%
607 1
< 0.1%
703 1
< 0.1%
721 1
< 0.1%
728 1
< 0.1%
828 1
< 0.1%
1029 1
< 0.1%
1036 1
< 0.1%
1100 1
< 0.1%
ValueCountFrequency (%)
760505847 1
< 0.1%
658672302 1
< 0.1%
652177271 1
< 0.1%
623279547 2
< 0.1%
533316061 1
< 0.1%
474544677 1
< 0.1%
460935665 1
< 0.1%
458991599 1
< 0.1%
448130642 1
< 0.1%
436471036 1
< 0.1%

genres
Text

Distinct914
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:03.785227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length64
Median length53
Mean length20.313107
Min length5

Characters and Unicode

Total characters102439
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique495 ?
Unique (%)9.8%

Sample

1st rowAction|Adventure|Fantasy|Sci-Fi
2nd rowAction|Adventure|Fantasy
3rd rowAction|Adventure|Thriller
4th rowAction|Thriller
5th rowDocumentary
ValueCountFrequency (%)
drama 236
 
4.7%
comedy 209
 
4.1%
comedy|drama 191
 
3.8%
comedy|drama|romance 187
 
3.7%
comedy|romance 158
 
3.1%
drama|romance 152
 
3.0%
crime|drama|thriller 101
 
2.0%
horror 71
 
1.4%
action|crime|drama|thriller 68
 
1.3%
action|crime|thriller 65
 
1.3%
Other values (904) 3605
71.5%
2024-04-11T11:25:04.200221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 10547
 
10.3%
| 9461
 
9.2%
a 9065
 
8.8%
e 7946
 
7.8%
m 7378
 
7.2%
i 6575
 
6.4%
o 6319
 
6.2%
y 4651
 
4.5%
n 4495
 
4.4%
t 4042
 
3.9%
Other values (25) 31960
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 77222
75.4%
Uppercase Letter 15131
 
14.8%
Math Symbol 9461
 
9.2%
Dash Punctuation 625
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 10547
13.7%
a 9065
11.7%
e 7946
10.3%
m 7378
9.6%
i 6575
8.5%
o 6319
8.2%
y 4651
 
6.0%
n 4495
 
5.8%
t 4042
 
5.2%
l 3508
 
4.5%
Other values (9) 12696
16.4%
Uppercase Letter
ValueCountFrequency (%)
C 2761
18.2%
D 2715
17.9%
A 2318
15.3%
F 1778
11.8%
T 1413
9.3%
R 1109
7.3%
M 846
 
5.6%
S 804
 
5.3%
H 772
 
5.1%
W 310
 
2.0%
Other values (4) 305
 
2.0%
Math Symbol
ValueCountFrequency (%)
| 9461
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 625
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 92353
90.2%
Common 10086
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 10547
 
11.4%
a 9065
 
9.8%
e 7946
 
8.6%
m 7378
 
8.0%
i 6575
 
7.1%
o 6319
 
6.8%
y 4651
 
5.0%
n 4495
 
4.9%
t 4042
 
4.4%
l 3508
 
3.8%
Other values (23) 27827
30.1%
Common
ValueCountFrequency (%)
| 9461
93.8%
- 625
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 10547
 
10.3%
| 9461
 
9.2%
a 9065
 
8.8%
e 7946
 
7.8%
m 7378
 
7.2%
i 6575
 
6.4%
o 6319
 
6.2%
y 4651
 
4.5%
n 4495
 
4.4%
t 4042
 
3.9%
Other values (25) 31960
31.2%
Distinct2097
Distinct (%)41.6%
Missing7
Missing (%)0.1%
Memory size39.5 KiB
2024-04-11T11:25:04.481215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length13.192415
Min length4

Characters and Unicode

Total characters66437
Distinct characters76
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1360 ?
Unique (%)27.0%

Sample

1st rowCCH Pounder
2nd rowJohnny Depp
3rd rowChristoph Waltz
4th rowTom Hardy
5th rowDoug Walker
ValueCountFrequency (%)
robert 109
 
1.0%
tom 93
 
0.9%
michael 89
 
0.9%
jason 59
 
0.6%
de 57
 
0.5%
james 54
 
0.5%
bruce 51
 
0.5%
steve 50
 
0.5%
jr 49
 
0.5%
niro 49
 
0.5%
Other values (2888) 9784
93.7%
2024-04-11T11:25:04.931211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6213
 
9.4%
a 5732
 
8.6%
5408
 
8.1%
n 4818
 
7.3%
r 4311
 
6.5%
i 4249
 
6.4%
o 3918
 
5.9%
l 3312
 
5.0%
t 2569
 
3.9%
s 2349
 
3.5%
Other values (66) 23558
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50016
75.3%
Uppercase Letter 10711
 
16.1%
Space Separator 5408
 
8.1%
Other Punctuation 227
 
0.3%
Dash Punctuation 73
 
0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6213
12.4%
a 5732
11.5%
n 4818
9.6%
r 4311
8.6%
i 4249
 
8.5%
o 3918
 
7.8%
l 3312
 
6.6%
t 2569
 
5.1%
s 2349
 
4.7%
h 1791
 
3.6%
Other values (32) 10754
21.5%
Uppercase Letter
ValueCountFrequency (%)
J 954
 
8.9%
M 912
 
8.5%
S 853
 
8.0%
C 818
 
7.6%
B 741
 
6.9%
D 728
 
6.8%
R 635
 
5.9%
H 524
 
4.9%
A 499
 
4.7%
L 490
 
4.6%
Other values (18) 3557
33.2%
Other Punctuation
ValueCountFrequency (%)
. 179
78.9%
' 48
 
21.1%
Decimal Number
ValueCountFrequency (%)
5 1
50.0%
0 1
50.0%
Space Separator
ValueCountFrequency (%)
5408
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 60727
91.4%
Common 5710
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6213
 
10.2%
a 5732
 
9.4%
n 4818
 
7.9%
r 4311
 
7.1%
i 4249
 
7.0%
o 3918
 
6.5%
l 3312
 
5.5%
t 2569
 
4.2%
s 2349
 
3.9%
h 1791
 
2.9%
Other values (60) 21465
35.3%
Common
ValueCountFrequency (%)
5408
94.7%
. 179
 
3.1%
- 73
 
1.3%
' 48
 
0.8%
5 1
 
< 0.1%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66357
99.9%
None 80
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6213
 
9.4%
a 5732
 
8.6%
5408
 
8.1%
n 4818
 
7.3%
r 4311
 
6.5%
i 4249
 
6.4%
o 3918
 
5.9%
l 3312
 
5.0%
t 2569
 
3.9%
s 2349
 
3.5%
Other values (48) 23478
35.4%
None
ValueCountFrequency (%)
é 20
25.0%
ë 15
18.8%
á 7
 
8.8%
í 6
 
7.5%
Ã¥ 5
 
6.2%
ç 5
 
6.2%
ø 4
 
5.0%
Ó 3
 
3.8%
à 2
 
2.5%
ü 2
 
2.5%
Other values (8) 11
13.8%
Distinct4917
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:05.257707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length87
Median length59
Mean length16.549673
Min length2

Characters and Unicode

Total characters83460
Distinct characters97
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4798 ?
Unique (%)95.1%

Sample

1st rowAvatar 
2nd rowPirates of the Caribbean: At World's End 
3rd rowSpectre 
4th rowThe Dark Knight Rises 
5th rowStar Wars: Episode VII - The Force Awakens 
ValueCountFrequency (%)
the 1606
 
11.5%
of 483
 
3.5%
a 193
 
1.4%
and 150
 
1.1%
in 123
 
0.9%
to 107
 
0.8%
2 104
 
0.7%
81
 
0.6%
man 66
 
0.5%
love 56
 
0.4%
Other values (4905) 10987
78.7%
2024-04-11T11:25:05.784702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10209
 
12.2%
e 7898
 
9.5%
  5043
 
6.0%
a 4859
 
5.8%
o 4669
 
5.6%
n 4141
 
5.0%
r 4135
 
5.0%
i 3933
 
4.7%
t 3818
 
4.6%
s 3007
 
3.6%
Other values (87) 31748
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54383
65.2%
Space Separator 15252
 
18.3%
Uppercase Letter 12232
 
14.7%
Other Punctuation 952
 
1.1%
Decimal Number 526
 
0.6%
Dash Punctuation 95
 
0.1%
Close Punctuation 5
 
< 0.1%
Open Punctuation 5
 
< 0.1%
Currency Symbol 4
 
< 0.1%
Other Number 2
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7898
14.5%
a 4859
 
8.9%
o 4669
 
8.6%
n 4141
 
7.6%
r 4135
 
7.6%
i 3933
 
7.2%
t 3818
 
7.0%
s 3007
 
5.5%
h 2975
 
5.5%
l 2538
 
4.7%
Other values (25) 12410
22.8%
Uppercase Letter
ValueCountFrequency (%)
T 1724
14.1%
S 1054
 
8.6%
M 821
 
6.7%
B 778
 
6.4%
D 727
 
5.9%
C 687
 
5.6%
A 664
 
5.4%
L 580
 
4.7%
H 569
 
4.7%
W 505
 
4.1%
Other values (17) 4123
33.7%
Other Punctuation
ValueCountFrequency (%)
: 371
39.0%
' 231
24.3%
. 145
 
15.2%
, 79
 
8.3%
& 61
 
6.4%
! 32
 
3.4%
? 16
 
1.7%
/ 8
 
0.8%
* 5
 
0.5%
# 2
 
0.2%
Other values (2) 2
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 147
27.9%
3 87
16.5%
0 86
16.3%
1 82
15.6%
4 35
 
6.7%
8 22
 
4.2%
5 21
 
4.0%
9 17
 
3.2%
7 15
 
2.9%
6 14
 
2.7%
Space Separator
ValueCountFrequency (%)
10209
66.9%
  5043
33.1%
Close Punctuation
ValueCountFrequency (%)
) 3
60.0%
] 2
40.0%
Open Punctuation
ValueCountFrequency (%)
( 3
60.0%
[ 2
40.0%
Currency Symbol
ValueCountFrequency (%)
¢ 2
50.0%
$ 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 95
100.0%
Other Number
ValueCountFrequency (%)
½ 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66615
79.8%
Common 16845
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7898
 
11.9%
a 4859
 
7.3%
o 4669
 
7.0%
n 4141
 
6.2%
r 4135
 
6.2%
i 3933
 
5.9%
t 3818
 
5.7%
s 3007
 
4.5%
h 2975
 
4.5%
l 2538
 
3.8%
Other values (52) 24642
37.0%
Common
ValueCountFrequency (%)
10209
60.6%
  5043
29.9%
: 371
 
2.2%
' 231
 
1.4%
2 147
 
0.9%
. 145
 
0.9%
- 95
 
0.6%
3 87
 
0.5%
0 86
 
0.5%
1 82
 
0.5%
Other values (25) 349
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78394
93.9%
None 5066
 
6.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10209
 
13.0%
e 7898
 
10.1%
a 4859
 
6.2%
o 4669
 
6.0%
n 4141
 
5.3%
r 4135
 
5.3%
i 3933
 
5.0%
t 3818
 
4.9%
s 3007
 
3.8%
h 2975
 
3.8%
Other values (72) 28750
36.7%
None
ValueCountFrequency (%)
  5043
99.5%
é 8
 
0.2%
¢ 2
 
< 0.1%
½ 2
 
< 0.1%
ä 1
 
< 0.1%
Æ 1
 
< 0.1%
è 1
 
< 0.1%
à 1
 
< 0.1%
· 1
 
< 0.1%
ü 1
 
< 0.1%
Other values (5) 5
 
0.1%

num_voted_users
Real number (ℝ)

HIGH CORRELATION 

Distinct4826
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83668.161
Minimum5
Maximum1689764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:05.985727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile514.6
Q18593.5
median34359
Q396309
95-th percentile332254.9
Maximum1689764
Range1689759
Interquartile range (IQR)87715.5

Descriptive statistics

Standard deviation138485.26
Coefficient of variation (CV)1.6551727
Kurtosis24.44552
Mean83668.161
Median Absolute Deviation (MAD)30816
Skewness4.0298711
Sum4.2193854 × 108
Variance1.9178166 × 1010
MonotonicityNot monotonic
2024-04-11T11:25:06.176825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 5
 
0.1%
6 4
 
0.1%
3119 3
 
0.1%
38 3
 
0.1%
53 3
 
0.1%
374 3
 
0.1%
6025 3
 
0.1%
62 3
 
0.1%
2541 3
 
0.1%
162 3
 
0.1%
Other values (4816) 5010
99.3%
ValueCountFrequency (%)
5 2
< 0.1%
6 4
0.1%
7 2
< 0.1%
8 3
0.1%
10 1
 
< 0.1%
13 1
 
< 0.1%
15 2
< 0.1%
16 1
 
< 0.1%
18 2
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
1689764 1
< 0.1%
1676169 1
< 0.1%
1468200 1
< 0.1%
1347461 1
< 0.1%
1324680 1
< 0.1%
1251222 1
< 0.1%
1238746 1
< 0.1%
1217752 1
< 0.1%
1215718 1
< 0.1%
1155770 1
< 0.1%

cast_total_fb_likes
Real number (ℝ)

HIGH CORRELATION 

Distinct3978
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9699.0639
Minimum0
Maximum656730
Zeros33
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:06.391381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile179
Q11411
median3090
Q313756.5
95-th percentile36927.7
Maximum656730
Range656730
Interquartile range (IQR)12345.5

Descriptive statistics

Standard deviation18163.799
Coefficient of variation (CV)1.8727373
Kurtosis361.25512
Mean9699.0639
Median Absolute Deviation (MAD)2302
Skewness12.831928
Sum48912379
Variance3.299236 × 108
MonotonicityNot monotonic
2024-04-11T11:25:06.644643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
 
0.7%
5 7
 
0.1%
2020 6
 
0.1%
2 6
 
0.1%
673 5
 
0.1%
29 5
 
0.1%
1044 5
 
0.1%
81 4
 
0.1%
964 4
 
0.1%
1136 4
 
0.1%
Other values (3968) 4964
98.4%
ValueCountFrequency (%)
0 33
0.7%
2 6
 
0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 7
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
656730 1
< 0.1%
303717 1
< 0.1%
283939 1
< 0.1%
263584 1
< 0.1%
261818 1
< 0.1%
170118 1
< 0.1%
140268 1
< 0.1%
137712 1
< 0.1%
120797 1
< 0.1%
108016 1
< 0.1%
Distinct3521
Distinct (%)70.1%
Missing23
Missing (%)0.5%
Memory size39.5 KiB
2024-04-11T11:25:07.033708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length13.082271
Min length3

Characters and Unicode

Total characters65673
Distinct characters81
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2648 ?
Unique (%)52.7%

Sample

1st rowWes Studi
2nd rowJack Davenport
3rd rowStephanie Sigman
4th rowJoseph Gordon-Levitt
5th rowPolly Walker
ValueCountFrequency (%)
michael 86
 
0.8%
john 80
 
0.8%
david 70
 
0.7%
james 69
 
0.7%
robert 46
 
0.4%
tom 43
 
0.4%
paul 42
 
0.4%
kevin 41
 
0.4%
peter 38
 
0.4%
scott 36
 
0.3%
Other values (4307) 9842
94.7%
2024-04-11T11:25:07.580756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6190
 
9.4%
a 5995
 
9.1%
5373
 
8.2%
n 4589
 
7.0%
r 4183
 
6.4%
i 3975
 
6.1%
o 3584
 
5.5%
l 3508
 
5.3%
t 2354
 
3.6%
s 2343
 
3.6%
Other values (71) 23579
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49295
75.1%
Uppercase Letter 10690
 
16.3%
Space Separator 5373
 
8.2%
Other Punctuation 234
 
0.4%
Dash Punctuation 79
 
0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6190
12.6%
a 5995
12.2%
n 4589
9.3%
r 4183
 
8.5%
i 3975
 
8.1%
o 3584
 
7.3%
l 3508
 
7.1%
t 2354
 
4.8%
s 2343
 
4.8%
h 1857
 
3.8%
Other values (34) 10717
21.7%
Uppercase Letter
ValueCountFrequency (%)
M 986
 
9.2%
J 832
 
7.8%
S 830
 
7.8%
B 806
 
7.5%
C 792
 
7.4%
D 653
 
6.1%
R 615
 
5.8%
A 589
 
5.5%
L 536
 
5.0%
K 464
 
4.3%
Other values (21) 3587
33.6%
Other Punctuation
ValueCountFrequency (%)
. 171
73.1%
' 63
 
26.9%
Decimal Number
ValueCountFrequency (%)
0 1
50.0%
5 1
50.0%
Space Separator
ValueCountFrequency (%)
5373
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 79
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 59985
91.3%
Common 5688
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6190
 
10.3%
a 5995
 
10.0%
n 4589
 
7.7%
r 4183
 
7.0%
i 3975
 
6.6%
o 3584
 
6.0%
l 3508
 
5.8%
t 2354
 
3.9%
s 2343
 
3.9%
h 1857
 
3.1%
Other values (65) 21407
35.7%
Common
ValueCountFrequency (%)
5373
94.5%
. 171
 
3.0%
- 79
 
1.4%
' 63
 
1.1%
0 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65537
99.8%
None 136
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6190
 
9.4%
a 5995
 
9.1%
5373
 
8.2%
n 4589
 
7.0%
r 4183
 
6.4%
i 3975
 
6.1%
o 3584
 
5.5%
l 3508
 
5.4%
t 2354
 
3.6%
s 2343
 
3.6%
Other values (48) 23443
35.8%
None
ValueCountFrequency (%)
é 49
36.0%
í 14
 
10.3%
á 13
 
9.6%
ó 9
 
6.6%
ë 7
 
5.1%
ü 7
 
5.1%
à 6
 
4.4%
è 4
 
2.9%
ç 3
 
2.2%
ô 3
 
2.2%
Other values (13) 21
15.4%

facenumber_in_poster
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)0.4%
Missing13
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.371173
Minimum0
Maximum43
Zeros2152
Zeros (%)42.7%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:07.756944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum43
Range43
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0135759
Coefficient of variation (CV)1.4685061
Kurtosis52.033735
Mean1.371173
Median Absolute Deviation (MAD)1
Skewness4.3847659
Sum6897
Variance4.054488
MonotonicityNot monotonic
2024-04-11T11:25:07.885087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 2152
42.7%
1 1251
24.8%
2 716
 
14.2%
3 380
 
7.5%
4 207
 
4.1%
5 114
 
2.3%
6 76
 
1.5%
7 48
 
1.0%
8 37
 
0.7%
9 18
 
0.4%
Other values (9) 31
 
0.6%
(Missing) 13
 
0.3%
ValueCountFrequency (%)
0 2152
42.7%
1 1251
24.8%
2 716
 
14.2%
3 380
 
7.5%
4 207
 
4.1%
5 114
 
2.3%
6 76
 
1.5%
7 48
 
1.0%
8 37
 
0.7%
9 18
 
0.4%
ValueCountFrequency (%)
43 1
 
< 0.1%
31 1
 
< 0.1%
19 1
 
< 0.1%
15 6
 
0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 4
 
0.1%
11 5
 
0.1%
10 10
0.2%
9 18
0.4%

plot_keywords
Text

MISSING 

Distinct4760
Distinct (%)97.3%
Missing153
Missing (%)3.0%
Memory size39.5 KiB
2024-04-11T11:25:08.170755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length149
Median length102
Mean length52.426994
Min length2

Characters and Unicode

Total characters256368
Distinct characters42
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4639 ?
Unique (%)94.9%

Sample

1st rowavatar|future|marine|native|paraplegic
2nd rowgoddess|marriage ceremony|marriage proposal|pirate|singapore
3rd rowbomb|espionage|sequel|spy|terrorist
4th rowdeception|imprisonment|lawlessness|police officer|terrorist plot
5th rowalien|american civil war|male nipple|mars|princess
ValueCountFrequency (%)
in 331
 
1.8%
of 222
 
1.2%
on 209
 
1.2%
the 191
 
1.1%
a 185
 
1.0%
to 180
 
1.0%
york 122
 
0.7%
based 106
 
0.6%
female 104
 
0.6%
by 99
 
0.5%
Other values (11486) 16269
90.3%
2024-04-11T11:25:08.704325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 24813
 
9.7%
a 19572
 
7.6%
| 19207
 
7.5%
i 18742
 
7.3%
r 18124
 
7.1%
t 16182
 
6.3%
n 15662
 
6.1%
o 15470
 
6.0%
s 13297
 
5.2%
13128
 
5.1%
Other values (32) 82171
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 222681
86.9%
Math Symbol 19207
 
7.5%
Space Separator 13128
 
5.1%
Decimal Number 1131
 
0.4%
Other Punctuation 219
 
0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 24813
11.1%
a 19572
 
8.8%
i 18742
 
8.4%
r 18124
 
8.1%
t 16182
 
7.3%
n 15662
 
7.0%
o 15470
 
6.9%
s 13297
 
6.0%
l 11203
 
5.0%
c 9458
 
4.2%
Other values (16) 60158
27.0%
Decimal Number
ValueCountFrequency (%)
1 284
25.1%
0 270
23.9%
9 222
19.6%
2 81
 
7.2%
8 65
 
5.7%
7 49
 
4.3%
5 47
 
4.2%
3 44
 
3.9%
6 38
 
3.4%
4 31
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 130
59.4%
' 89
40.6%
Math Symbol
ValueCountFrequency (%)
| 19207
100.0%
Space Separator
ValueCountFrequency (%)
13128
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 222681
86.9%
Common 33687
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 24813
11.1%
a 19572
 
8.8%
i 18742
 
8.4%
r 18124
 
8.1%
t 16182
 
7.3%
n 15662
 
7.0%
o 15470
 
6.9%
s 13297
 
6.0%
l 11203
 
5.0%
c 9458
 
4.2%
Other values (16) 60158
27.0%
Common
ValueCountFrequency (%)
| 19207
57.0%
13128
39.0%
1 284
 
0.8%
0 270
 
0.8%
9 222
 
0.7%
. 130
 
0.4%
' 89
 
0.3%
2 81
 
0.2%
8 65
 
0.2%
7 49
 
0.1%
Other values (6) 162
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 256368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 24813
 
9.7%
a 19572
 
7.6%
| 19207
 
7.5%
i 18742
 
7.3%
r 18124
 
7.1%
t 16182
 
6.3%
n 15662
 
6.1%
o 15470
 
6.0%
s 13297
 
5.2%
13128
 
5.1%
Other values (32) 82171
32.1%
Distinct4919
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:08.994364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length52
Median length52
Mean length52
Min length52

Characters and Unicode

Total characters262236
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4802 ?
Unique (%)95.2%

Sample

1st rowhttp://www.imdb.com/title/tt0499549/?ref_=fn_tt_tt_1
2nd rowhttp://www.imdb.com/title/tt0449088/?ref_=fn_tt_tt_1
3rd rowhttp://www.imdb.com/title/tt2379713/?ref_=fn_tt_tt_1
4th rowhttp://www.imdb.com/title/tt1345836/?ref_=fn_tt_tt_1
5th rowhttp://www.imdb.com/title/tt5289954/?ref_=fn_tt_tt_1
ValueCountFrequency (%)
http://www.imdb.com/title/tt0232500/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt3332064/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt0360717/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt2224026/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt1976009/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt0077651/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt2638144/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt4651520/?ref_=fn_tt_tt_1 2
 
< 0.1%
http://www.imdb.com/title/tt1051904/?ref_=fn_tt_tt_1 2
 
< 0.1%
http://www.imdb.com/title/tt1343727/?ref_=fn_tt_tt_1 2
 
< 0.1%
Other values (4909) 5016
99.5%
2024-04-11T11:25:09.401252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 50430
19.2%
/ 25215
 
9.6%
_ 20172
 
7.7%
w 15129
 
5.8%
. 10086
 
3.8%
i 10086
 
3.8%
m 10086
 
3.8%
f 10086
 
3.8%
e 10086
 
3.8%
1 9894
 
3.8%
Other values (21) 90966
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 151290
57.7%
Other Punctuation 45387
 
17.3%
Decimal Number 40344
 
15.4%
Connector Punctuation 20172
 
7.7%
Math Symbol 5043
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 50430
33.3%
w 15129
 
10.0%
i 10086
 
6.7%
m 10086
 
6.7%
f 10086
 
6.7%
e 10086
 
6.7%
n 5043
 
3.3%
r 5043
 
3.3%
h 5043
 
3.3%
l 5043
 
3.3%
Other values (5) 25215
16.7%
Decimal Number
ValueCountFrequency (%)
1 9894
24.5%
0 6821
16.9%
2 3668
 
9.1%
3 3246
 
8.0%
4 3177
 
7.9%
8 2911
 
7.2%
9 2726
 
6.8%
6 2722
 
6.7%
7 2702
 
6.7%
5 2477
 
6.1%
Other Punctuation
ValueCountFrequency (%)
/ 25215
55.6%
. 10086
 
22.2%
? 5043
 
11.1%
: 5043
 
11.1%
Connector Punctuation
ValueCountFrequency (%)
_ 20172
100.0%
Math Symbol
ValueCountFrequency (%)
= 5043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 151290
57.7%
Common 110946
42.3%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 25215
22.7%
_ 20172
18.2%
. 10086
 
9.1%
1 9894
 
8.9%
0 6821
 
6.1%
= 5043
 
4.5%
? 5043
 
4.5%
: 5043
 
4.5%
2 3668
 
3.3%
3 3246
 
2.9%
Other values (6) 16715
15.1%
Latin
ValueCountFrequency (%)
t 50430
33.3%
w 15129
 
10.0%
i 10086
 
6.7%
m 10086
 
6.7%
f 10086
 
6.7%
e 10086
 
6.7%
n 5043
 
3.3%
r 5043
 
3.3%
h 5043
 
3.3%
l 5043
 
3.3%
Other values (5) 25215
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 262236
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 50430
19.2%
/ 25215
 
9.6%
_ 20172
 
7.7%
w 15129
 
5.8%
. 10086
 
3.8%
i 10086
 
3.8%
m 10086
 
3.8%
f 10086
 
3.8%
e 10086
 
3.8%
1 9894
 
3.8%
Other values (21) 90966
34.7%

num_user_for_reviews
Real number (ℝ)

HIGH CORRELATION 

Distinct954
Distinct (%)19.0%
Missing21
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean272.77081
Minimum1
Maximum5060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:09.587275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q165
median156
Q3326
95-th percentile907.8
Maximum5060
Range5059
Interquartile range (IQR)261

Descriptive statistics

Standard deviation377.98289
Coefficient of variation (CV)1.385716
Kurtosis26.438297
Mean272.77081
Median Absolute Deviation (MAD)113
Skewness4.1214752
Sum1369855
Variance142871.06
MonotonicityNot monotonic
2024-04-11T11:25:09.768349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 51
 
1.0%
3 33
 
0.7%
2 32
 
0.6%
26 32
 
0.6%
10 29
 
0.6%
6 28
 
0.6%
50 26
 
0.5%
32 25
 
0.5%
8 25
 
0.5%
31 24
 
0.5%
Other values (944) 4717
93.5%
ValueCountFrequency (%)
1 51
1.0%
2 32
0.6%
3 33
0.7%
4 23
0.5%
5 19
 
0.4%
6 28
0.6%
7 17
 
0.3%
8 25
0.5%
9 23
0.5%
10 29
0.6%
ValueCountFrequency (%)
5060 1
< 0.1%
4667 1
< 0.1%
4144 1
< 0.1%
3646 1
< 0.1%
3597 1
< 0.1%
3516 1
< 0.1%
3400 1
< 0.1%
3286 1
< 0.1%
3189 1
< 0.1%
3054 1
< 0.1%

language
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct47
Distinct (%)0.9%
Missing12
Missing (%)0.2%
Memory size39.5 KiB
English
4704 
French
 
73
Spanish
 
40
Hindi
 
28
Mandarin
 
26
Other values (42)
 
160

Length

Max length10
Median length7
Mean length6.9807195
Min length4

Characters and Unicode

Total characters35120
Distinct characters43
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)0.4%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English 4704
93.3%
French 73
 
1.4%
Spanish 40
 
0.8%
Hindi 28
 
0.6%
Mandarin 26
 
0.5%
German 19
 
0.4%
Japanese 18
 
0.4%
Cantonese 11
 
0.2%
Russian 11
 
0.2%
Italian 11
 
0.2%
Other values (37) 90
 
1.8%
(Missing) 12
 
0.2%

Length

2024-04-11T11:25:09.926987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english 4704
93.5%
french 73
 
1.5%
spanish 40
 
0.8%
hindi 28
 
0.6%
mandarin 26
 
0.5%
german 19
 
0.4%
japanese 18
 
0.4%
cantonese 11
 
0.2%
russian 11
 
0.2%
italian 11
 
0.2%
Other values (37) 90
 
1.8%

Most occurring characters

ValueCountFrequency (%)
n 5032
14.3%
i 4906
14.0%
h 4845
13.8%
s 4828
13.7%
l 4731
13.5%
g 4722
13.4%
E 4704
13.4%
a 252
 
0.7%
e 217
 
0.6%
r 160
 
0.5%
Other values (33) 723
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30089
85.7%
Uppercase Letter 5031
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 5032
16.7%
i 4906
16.3%
h 4845
16.1%
s 4828
16.0%
l 4731
15.7%
g 4722
15.7%
a 252
 
0.8%
e 217
 
0.7%
r 160
 
0.5%
c 88
 
0.3%
Other values (13) 308
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
E 4704
93.5%
F 74
 
1.5%
S 47
 
0.9%
H 34
 
0.7%
M 28
 
0.6%
G 20
 
0.4%
J 18
 
0.4%
P 17
 
0.3%
C 15
 
0.3%
I 15
 
0.3%
Other values (10) 59
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 35120
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 5032
14.3%
i 4906
14.0%
h 4845
13.8%
s 4828
13.7%
l 4731
13.5%
g 4722
13.4%
E 4704
13.4%
a 252
 
0.7%
e 217
 
0.6%
r 160
 
0.5%
Other values (33) 723
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 5032
14.3%
i 4906
14.0%
h 4845
13.8%
s 4828
13.7%
l 4731
13.5%
g 4722
13.4%
E 4704
13.4%
a 252
 
0.7%
e 217
 
0.6%
r 160
 
0.5%
Other values (33) 723
 
2.1%
Distinct65
Distinct (%)1.3%
Missing5
Missing (%)0.1%
Memory size39.5 KiB
2024-04-11T11:25:10.142373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length20
Median length3
Mean length3.4892815
Min length2

Characters and Unicode

Total characters17579
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.6%

Sample

1st rowUSA
2nd rowUSA
3rd rowUK
4th rowUSA
5th rowUSA
ValueCountFrequency (%)
usa 3807
74.6%
uk 448
 
8.8%
france 154
 
3.0%
canada 126
 
2.5%
germany 100
 
2.0%
australia 55
 
1.1%
india 34
 
0.7%
spain 33
 
0.6%
china 30
 
0.6%
italy 23
 
0.5%
Other values (63) 294
 
5.8%
2024-04-11T11:25:10.532966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 4257
24.2%
A 3877
22.1%
S 3874
22.0%
a 1093
 
6.2%
n 638
 
3.6%
K 481
 
2.7%
e 410
 
2.3%
r 404
 
2.3%
i 249
 
1.4%
d 218
 
1.2%
Other values (37) 2078
11.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13165
74.9%
Lowercase Letter 4348
 
24.7%
Space Separator 66
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1093
25.1%
n 638
14.7%
e 410
 
9.4%
r 404
 
9.3%
i 249
 
5.7%
d 218
 
5.0%
c 193
 
4.4%
l 154
 
3.5%
y 139
 
3.2%
m 126
 
2.9%
Other values (14) 724
16.7%
Uppercase Letter
ValueCountFrequency (%)
U 4257
32.3%
A 3877
29.4%
S 3874
29.4%
K 481
 
3.7%
C 163
 
1.2%
F 155
 
1.2%
G 103
 
0.8%
I 81
 
0.6%
N 30
 
0.2%
J 23
 
0.2%
Other values (12) 121
 
0.9%
Space Separator
ValueCountFrequency (%)
66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17513
99.6%
Common 66
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 4257
24.3%
A 3877
22.1%
S 3874
22.1%
a 1093
 
6.2%
n 638
 
3.6%
K 481
 
2.7%
e 410
 
2.3%
r 404
 
2.3%
i 249
 
1.4%
d 218
 
1.2%
Other values (36) 2012
11.5%
Common
ValueCountFrequency (%)
66
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17579
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 4257
24.2%
A 3877
22.1%
S 3874
22.0%
a 1093
 
6.2%
n 638
 
3.6%
K 481
 
2.7%
e 410
 
2.3%
r 404
 
2.3%
i 249
 
1.4%
d 218
 
1.2%
Other values (37) 2078
11.8%

content_rating
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct18
Distinct (%)0.4%
Missing303
Missing (%)6.0%
Memory size39.5 KiB
R
2118 
PG-13
1461 
PG
701 
Not Rated
 
116
G
 
112
Other values (13)
232 

Length

Max length9
Median length8
Mean length2.8139241
Min length1

Characters and Unicode

Total characters13338
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPG-13
2nd rowPG-13
3rd rowPG-13
4th rowPG-13
5th rowPG-13

Common Values

ValueCountFrequency (%)
R 2118
42.0%
PG-13 1461
29.0%
PG 701
 
13.9%
Not Rated 116
 
2.3%
G 112
 
2.2%
Unrated 62
 
1.2%
Approved 55
 
1.1%
TV-14 30
 
0.6%
TV-MA 20
 
0.4%
TV-PG 13
 
0.3%
Other values (8) 52
 
1.0%
(Missing) 303
 
6.0%

Length

2024-04-11T11:25:10.712384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r 2118
43.6%
pg-13 1461
30.1%
pg 701
 
14.4%
not 116
 
2.4%
rated 116
 
2.4%
g 112
 
2.3%
unrated 62
 
1.3%
approved 55
 
1.1%
tv-14 30
 
0.6%
tv-ma 20
 
0.4%
Other values (9) 65
 
1.3%

Most occurring characters

ValueCountFrequency (%)
G 2303
17.3%
R 2234
16.7%
P 2190
16.4%
- 1543
11.6%
1 1498
11.2%
3 1461
11.0%
t 294
 
2.2%
e 242
 
1.8%
d 242
 
1.8%
a 187
 
1.4%
Other values (18) 1144
8.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7184
53.9%
Decimal Number 2997
22.5%
Dash Punctuation 1543
 
11.6%
Lowercase Letter 1498
 
11.2%
Space Separator 116
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2303
32.1%
R 2234
31.1%
P 2190
30.5%
N 123
 
1.7%
V 75
 
1.0%
T 75
 
1.0%
A 75
 
1.0%
U 62
 
0.9%
M 25
 
0.3%
X 13
 
0.2%
Other values (2) 9
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
t 294
19.6%
e 242
16.2%
d 242
16.2%
a 187
12.5%
o 171
11.4%
r 117
 
7.8%
p 110
 
7.3%
n 62
 
4.1%
v 55
 
3.7%
s 18
 
1.2%
Decimal Number
ValueCountFrequency (%)
1 1498
50.0%
3 1461
48.7%
4 30
 
1.0%
7 8
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 1543
100.0%
Space Separator
ValueCountFrequency (%)
116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8682
65.1%
Common 4656
34.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 2303
26.5%
R 2234
25.7%
P 2190
25.2%
t 294
 
3.4%
e 242
 
2.8%
d 242
 
2.8%
a 187
 
2.2%
o 171
 
2.0%
N 123
 
1.4%
r 117
 
1.3%
Other values (12) 579
 
6.7%
Common
ValueCountFrequency (%)
- 1543
33.1%
1 1498
32.2%
3 1461
31.4%
116
 
2.5%
4 30
 
0.6%
7 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13338
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 2303
17.3%
R 2234
16.7%
P 2190
16.4%
- 1543
11.6%
1 1498
11.2%
3 1461
11.0%
t 294
 
2.2%
e 242
 
1.8%
d 242
 
1.8%
a 187
 
1.4%
Other values (18) 1144
8.6%

budget
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct444
Distinct (%)9.6%
Missing406
Missing (%)8.1%
Infinite0
Infinite (%)0.0%
Mean39389278
Minimum218
Maximum1.22155 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:10.881379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum218
5-th percentile500000
Q16000000
median20000000
Q343000000
95-th percentile1.3 × 108
Maximum1.22155 × 1010
Range1.22155 × 1010
Interquartile range (IQR)37000000

Descriptive statistics

Standard deviation2.0424729 × 108
Coefficient of variation (CV)5.1853524
Kurtosis2773.1951
Mean39389278
Median Absolute Deviation (MAD)16000000
Skewness48.577517
Sum1.8264808 × 1011
Variance4.1716954 × 1016
MonotonicityNot monotonic
2024-04-11T11:25:11.087924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000000 179
 
3.5%
15000000 146
 
2.9%
30000000 146
 
2.9%
25000000 143
 
2.8%
10000000 142
 
2.8%
40000000 133
 
2.6%
35000000 121
 
2.4%
5000000 111
 
2.2%
50000000 104
 
2.1%
12000000 96
 
1.9%
Other values (434) 3316
65.8%
(Missing) 406
 
8.1%
ValueCountFrequency (%)
218 1
 
< 0.1%
1100 1
 
< 0.1%
1400 1
 
< 0.1%
3250 1
 
< 0.1%
4500 1
 
< 0.1%
7000 3
0.1%
9000 1
 
< 0.1%
10000 3
0.1%
13000 1
 
< 0.1%
14000 1
 
< 0.1%
ValueCountFrequency (%)
1.22155 × 10101
< 0.1%
4200000000 1
< 0.1%
2500000000 1
< 0.1%
2400000000 1
< 0.1%
2127519898 1
< 0.1%
1100000000 1
< 0.1%
1000000000 1
< 0.1%
700000000 2
< 0.1%
600000000 1
< 0.1%
553632000 1
< 0.1%

title_year
Real number (ℝ)

MISSING 

Distinct91
Distinct (%)1.8%
Missing108
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean2002.4705
Minimum1916
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:11.286921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1916
5-th percentile1979
Q11999
median2005
Q32011
95-th percentile2015
Maximum2016
Range100
Interquartile range (IQR)12

Descriptive statistics

Standard deviation12.474599
Coefficient of variation (CV)0.0062296043
Kurtosis7.4392126
Mean2002.4705
Median Absolute Deviation (MAD)6
Skewness-2.2922733
Sum9882192
Variance155.61562
MonotonicityNot monotonic
2024-04-11T11:25:11.463917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2009 260
 
5.2%
2014 252
 
5.0%
2006 239
 
4.7%
2013 237
 
4.7%
2010 230
 
4.6%
2015 226
 
4.5%
2008 225
 
4.5%
2011 225
 
4.5%
2012 221
 
4.4%
2005 221
 
4.4%
Other values (81) 2599
51.5%
ValueCountFrequency (%)
1916 1
< 0.1%
1920 1
< 0.1%
1925 1
< 0.1%
1927 1
< 0.1%
1929 2
< 0.1%
1930 1
< 0.1%
1932 1
< 0.1%
1933 2
< 0.1%
1934 1
< 0.1%
1935 1
< 0.1%
ValueCountFrequency (%)
2016 106
2.1%
2015 226
4.5%
2014 252
5.0%
2013 237
4.7%
2012 221
4.4%
2011 225
4.5%
2010 230
4.6%
2009 260
5.2%
2008 225
4.5%
2007 204
4.0%

actor_2_fb_likes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct917
Distinct (%)18.2%
Missing13
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1651.7545
Minimum0
Maximum137000
Zeros55
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:11.646910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26
Q1281
median595
Q3918
95-th percentile11000
Maximum137000
Range137000
Interquartile range (IQR)637

Descriptive statistics

Standard deviation4042.4389
Coefficient of variation (CV)2.4473606
Kurtosis256.79519
Mean1651.7545
Median Absolute Deviation (MAD)317
Skewness9.8847332
Sum8308325
Variance16341312
MonotonicityNot monotonic
2024-04-11T11:25:11.827910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 309
 
6.1%
11000 111
 
2.2%
2000 100
 
2.0%
3000 76
 
1.5%
0 55
 
1.1%
10000 47
 
0.9%
14000 41
 
0.8%
13000 40
 
0.8%
826 37
 
0.7%
4000 34
 
0.7%
Other values (907) 4180
82.9%
ValueCountFrequency (%)
0 55
1.1%
2 14
 
0.3%
3 14
 
0.3%
4 12
 
0.2%
5 10
 
0.2%
6 7
 
0.1%
7 4
 
0.1%
8 9
 
0.2%
9 13
 
0.3%
10 9
 
0.2%
ValueCountFrequency (%)
137000 1
 
< 0.1%
29000 1
 
< 0.1%
27000 2
 
< 0.1%
25000 3
 
0.1%
23000 6
0.1%
22000 11
0.2%
21000 4
 
0.1%
20000 6
0.1%
19000 7
0.1%
18000 9
0.2%

imdb_score
Real number (ℝ)

Distinct78
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4421376
Minimum1.6
Maximum9.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:12.011837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.4
Q15.8
median6.6
Q37.2
95-th percentile8.09
Maximum9.5
Range7.9
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.1251159
Coefficient of variation (CV)0.17464946
Kurtosis0.93569151
Mean6.4421376
Median Absolute Deviation (MAD)0.7
Skewness-0.74147134
Sum32487.7
Variance1.2658857
MonotonicityNot monotonic
2024-04-11T11:25:12.179833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7 223
 
4.4%
6.6 201
 
4.0%
7.2 195
 
3.9%
6.5 186
 
3.7%
6.4 185
 
3.7%
7 184
 
3.6%
7.3 184
 
3.6%
6.8 181
 
3.6%
7.1 181
 
3.6%
6.1 179
 
3.5%
Other values (68) 3144
62.3%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
1.7 1
 
< 0.1%
1.9 3
0.1%
2 2
< 0.1%
2.1 3
0.1%
2.2 3
0.1%
2.3 3
0.1%
2.4 2
< 0.1%
2.5 2
< 0.1%
2.6 2
< 0.1%
ValueCountFrequency (%)
9.5 1
 
< 0.1%
9.3 1
 
< 0.1%
9.2 1
 
< 0.1%
9.1 3
 
0.1%
9 3
 
0.1%
8.9 5
 
0.1%
8.8 7
 
0.1%
8.7 13
0.3%
8.6 15
0.3%
8.5 24
0.5%

aspect_ratio
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)0.5%
Missing329
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean2.2204031
Minimum1.18
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:12.334834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.18
5-th percentile1.66
Q11.85
median2.35
Q32.35
95-th percentile2.35
Maximum16
Range14.82
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation1.3851125
Coefficient of variation (CV)0.62381131
Kurtosis90.653221
Mean2.2204031
Median Absolute Deviation (MAD)0
Skewness9.3900563
Sum10466.98
Variance1.9185367
MonotonicityNot monotonic
2024-04-11T11:25:12.462867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2.35 2360
46.8%
1.85 1906
37.8%
1.78 110
 
2.2%
1.37 100
 
2.0%
1.33 68
 
1.3%
1.66 64
 
1.3%
16 45
 
0.9%
2.2 15
 
0.3%
2.39 15
 
0.3%
4 7
 
0.1%
Other values (12) 24
 
0.5%
(Missing) 329
 
6.5%
ValueCountFrequency (%)
1.18 1
 
< 0.1%
1.2 1
 
< 0.1%
1.33 68
1.3%
1.37 100
2.0%
1.44 1
 
< 0.1%
1.5 2
 
< 0.1%
1.66 64
1.3%
1.75 3
 
0.1%
1.77 1
 
< 0.1%
1.78 110
2.2%
ValueCountFrequency (%)
16 45
 
0.9%
4 7
 
0.1%
2.76 3
 
0.1%
2.55 2
 
< 0.1%
2.4 3
 
0.1%
2.39 15
 
0.3%
2.35 2360
46.8%
2.24 1
 
< 0.1%
2.2 15
 
0.3%
2 5
 
0.1%

movie_fb_likes
Real number (ℝ)

ZEROS 

Distinct876
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7525.9645
Minimum0
Maximum349000
Zeros2181
Zeros (%)43.2%
Negative0
Negative (%)0.0%
Memory size39.5 KiB
2024-04-11T11:25:12.634919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median166
Q33000
95-th percentile40000
Maximum349000
Range349000
Interquartile range (IQR)3000

Descriptive statistics

Standard deviation19320.445
Coefficient of variation (CV)2.567172
Kurtosis41.334437
Mean7525.9645
Median Absolute Deviation (MAD)166
Skewness5.0589269
Sum37953439
Variance3.732796 × 108
MonotonicityNot monotonic
2024-04-11T11:25:12.809893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2181
43.2%
1000 109
 
2.2%
11000 83
 
1.6%
10000 81
 
1.6%
12000 62
 
1.2%
13000 58
 
1.2%
2000 56
 
1.1%
15000 53
 
1.1%
14000 50
 
1.0%
16000 47
 
0.9%
Other values (866) 2263
44.9%
ValueCountFrequency (%)
0 2181
43.2%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 5
 
0.1%
5 2
 
< 0.1%
7 3
 
0.1%
8 1
 
< 0.1%
9 3
 
0.1%
10 2
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
349000 1
< 0.1%
199000 1
< 0.1%
197000 1
< 0.1%
191000 1
< 0.1%
190000 1
< 0.1%
175000 1
< 0.1%
166000 1
< 0.1%
165000 1
< 0.1%
164000 1
< 0.1%
153000 1
< 0.1%

Interactions

2024-04-11T11:24:53.079668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:12.354554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:15.227429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:18.641046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:21.172306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:23.632192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:26.237270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:28.812285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:32.003227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:34.636229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:37.117747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:39.652120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:42.202168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:44.697387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:47.384803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:50.472752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:53.273668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:12.590595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:16.256925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:18.813272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:21.344829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:23.803189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:26.398266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:29.084279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:32.169714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:34.808285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:37.294770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:39.820117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:42.378624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:44.888382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:47.574799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:50.641749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:53.451660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:12.808064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:16.395927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:18.983271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:21.485829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:23.956780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:26.547789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:29.255280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:32.322260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:34.945247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:37.454741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:40.008113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:42.537587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:45.047380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:47.735795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:50.789751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:53.596663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:12.980574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:16.546963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:19.126268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:21.636825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:24.107341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:26.690785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:29.422280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:32.474296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:35.092278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:37.604737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:40.167108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:42.691832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:45.206379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:47.882795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:50.960675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:53.740658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:13.149572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:16.695923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:19.272265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:21.773852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:24.256339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:26.843785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:29.607725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:32.626261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:35.228245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:37.752202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:40.306662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:42.829823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:45.364414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:48.033796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:51.111164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:53.901167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:13.317399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:16.859642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:19.443263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:21.928850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:24.408331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:27.007034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:29.782723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:32.778257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:35.381241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:37.909200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:40.461220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:42.981336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:45.520377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:48.195791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:51.271365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:54.071703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:13.496397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:17.025265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:19.607262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:22.078195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:24.573329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:27.182034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:29.942720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:32.950257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:35.561244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:38.075198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:40.619305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:43.136849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:45.680881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:48.381306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:51.451002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:54.238729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:13.686395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:17.189781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:19.782288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:22.233190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:24.742329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:27.344030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:30.106754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:33.136249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:35.720237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:38.234194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:40.782945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:43.327784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:45.882247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:49.044815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:51.616998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:54.399941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:13.860393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:17.353862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:19.931260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:22.393187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:24.917324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:27.516053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:30.269719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:33.307251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:35.881749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:38.391195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:40.939907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:43.487781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:46.062244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:49.202811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:51.776997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:54.550935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:14.010391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:17.497118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:20.068291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:22.531206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:25.063359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:27.662054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:30.833419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:33.466245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:36.020784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:38.536190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:41.080904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:43.619781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:46.205247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:49.353810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:51.926994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:54.708228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:14.177388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:17.660115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:20.219291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:22.682205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:25.224339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:27.829899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:30.998690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:33.650243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:36.165749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:38.687191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:41.241904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:43.762778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:46.373240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:49.510807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:52.095289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:54.886184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:14.342475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:17.825112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:20.368288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:22.850200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:25.391338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:28.005935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:31.162721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:33.819240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:36.316739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:38.854729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:41.389899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:43.918776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:46.547237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:49.663809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:52.256777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:55.038459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:14.500438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:17.983108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:20.510287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:23.006238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:25.543280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:28.155926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:31.328200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:33.971238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:36.457302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:39.000733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:41.554902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:44.056776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:46.697237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:49.819323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:52.424775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:55.206322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:14.714459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:18.146107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:20.689284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:23.161196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:25.732277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:28.321433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:31.503195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:34.140235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:36.627300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:39.170123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:41.715931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:44.231774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:46.866235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:49.983245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:52.588279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:55.374296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:14.886467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:18.306104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:20.852281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:23.313195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:25.901277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:28.480464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:31.664193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:34.303231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:36.787298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:39.322120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:41.872804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:44.385769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:47.031234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:50.150246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:52.743672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:55.561916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:15.055432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:18.485128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:21.008278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:23.468193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:26.063272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:28.643286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:31.831189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:34.472230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:36.943344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:39.484118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:42.037243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:44.538390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:47.193230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:50.305242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:24:52.901706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-04-11T11:25:13.585887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
actor_1_fb_likesactor_2_fb_likesactor_3_fb_likesaspect_ratiobudgetcast_total_fb_likescolorcontent_ratingdirector_fb_likesdurationfacenumber_in_postergrossimdb_scorelanguagemovie_fb_likesnum_critic_for_reviewsnum_user_for_reviewsnum_voted_userstitle_year
actor_1_fb_likes1.0000.7650.6560.1760.4210.9540.0000.0000.1640.2330.1140.3510.0480.0000.1080.3750.3880.4520.078
actor_2_fb_likes0.7651.0000.8600.1530.4140.8400.0000.0000.1380.1910.1060.382-0.0270.0000.1010.3180.3520.4040.070
actor_3_fb_likes0.6560.8601.0000.1180.3770.7700.0000.0000.1160.1530.1130.371-0.0620.0000.0860.2660.3200.3570.049
aspect_ratio0.1760.1530.1181.0000.3260.1770.0350.5260.0900.2400.0320.135-0.0170.0000.0880.2440.1580.1830.279
budget0.4210.4140.3770.3261.0000.4450.0000.0000.1980.3660.0280.644-0.0460.5880.1040.4570.4800.5440.103
cast_total_fb_likes0.9540.8400.7700.1770.4451.0000.0000.0000.1690.2350.1310.3900.0220.0000.1140.3720.3960.4630.081
color0.0000.0000.0000.0350.0000.0001.0000.277-0.018-0.034-0.0000.070-0.1520.1030.0290.013-0.022-0.0000.168
content_rating0.0000.0000.0000.5260.0000.0000.2771.0000.010-0.0080.031-0.2580.0810.070-0.0000.0340.0340.0030.012
director_fb_likes0.1640.1380.1160.0900.1980.169-0.0180.0101.0000.2060.0120.1840.1350.1550.0420.2400.2540.275-0.042
duration0.2330.1910.1530.2400.3660.235-0.034-0.0080.2061.0000.0400.2680.3160.2930.1010.3060.3740.368-0.090
facenumber_in_poster0.1140.1060.1130.0320.0280.131-0.0000.0310.0120.0401.000-0.021-0.0870.021-0.013-0.072-0.092-0.0390.065
gross0.3510.3820.3710.1350.6440.3900.070-0.2580.1840.268-0.0211.0000.0960.0000.1180.4710.6050.700-0.003
imdb_score0.048-0.027-0.062-0.017-0.0460.022-0.1520.0810.1350.316-0.0870.0961.0000.0790.1290.2870.3230.390-0.160
language0.0000.0000.0000.0000.5880.0000.1030.0700.1550.2930.0210.0000.0791.000-0.008-0.078-0.132-0.1100.055
movie_fb_likes0.1080.1010.0860.0880.1040.1140.029-0.0000.0420.101-0.0130.1180.129-0.0081.0000.2710.1580.2010.261
num_critic_for_reviews0.3750.3180.2660.2440.4570.3720.0130.0340.2400.306-0.0720.4710.287-0.0780.2711.0000.7910.8170.281
num_user_for_reviews0.3880.3520.3200.1580.4800.396-0.0220.0340.2540.374-0.0920.6050.323-0.1320.1580.7911.0000.900-0.136
num_voted_users0.4520.4040.3570.1830.5440.463-0.0000.0030.2750.368-0.0390.7000.390-0.1100.2010.8170.9001.000-0.041
title_year0.0780.0700.0490.2790.1030.0810.1680.012-0.042-0.0900.065-0.003-0.1600.0550.2610.281-0.136-0.0411.000

Missing values

2024-04-11T11:24:55.859919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-11T11:24:56.518029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-11T11:24:58.369921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

colordirector_namenum_critic_for_reviewsdurationdirector_fb_likesactor_3_fb_likesactor_2_nameactor_1_fb_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_fb_likesactor_3_namefacenumber_in_posterplot_keywordsmovie_imdb_linknum_user_for_reviewslanguagecountrycontent_ratingbudgettitle_yearactor_2_fb_likesimdb_scoreaspect_ratiomovie_fb_likes
0ColorJames Cameron723.0178.00.0855.0Joel David Moore1000.0760505847.0Action|Adventure|Fantasy|Sci-FiCCH PounderAvatar8862044834Wes Studi0.0avatar|future|marine|native|paraplegichttp://www.imdb.com/title/tt0499549/?ref_=fn_tt_tt_13054.0EnglishUSAPG-13237000000.02009.0936.07.91.7833000
1ColorGore Verbinski302.0169.0563.01000.0Orlando Bloom40000.0309404152.0Action|Adventure|FantasyJohnny DeppPirates of the Caribbean: At World's End47122048350Jack Davenport0.0goddess|marriage ceremony|marriage proposal|pirate|singaporehttp://www.imdb.com/title/tt0449088/?ref_=fn_tt_tt_11238.0EnglishUSAPG-13300000000.02007.05000.07.12.350
2ColorSam Mendes602.0148.00.0161.0Rory Kinnear11000.0200074175.0Action|Adventure|ThrillerChristoph WaltzSpectre27586811700Stephanie Sigman1.0bomb|espionage|sequel|spy|terroristhttp://www.imdb.com/title/tt2379713/?ref_=fn_tt_tt_1994.0EnglishUKPG-13245000000.02015.0393.06.82.3585000
3ColorChristopher Nolan813.0164.022000.023000.0Christian Bale27000.0448130642.0Action|ThrillerTom HardyThe Dark Knight Rises1144337106759Joseph Gordon-Levitt0.0deception|imprisonment|lawlessness|police officer|terrorist plothttp://www.imdb.com/title/tt1345836/?ref_=fn_tt_tt_12701.0EnglishUSAPG-13250000000.02012.023000.08.52.35164000
4NaNDoug WalkerNaNNaN131.0NaNRob Walker131.0NaNDocumentaryDoug WalkerStar Wars: Episode VII - The Force Awakens8143NaN0.0NaNhttp://www.imdb.com/title/tt5289954/?ref_=fn_tt_tt_1NaNNaNNaNNaNNaNNaN12.07.1NaN0
5ColorAndrew Stanton462.0132.0475.0530.0Samantha Morton640.073058679.0Action|Adventure|Sci-FiDaryl SabaraJohn Carter2122041873Polly Walker1.0alien|american civil war|male nipple|mars|princesshttp://www.imdb.com/title/tt0401729/?ref_=fn_tt_tt_1738.0EnglishUSAPG-13263700000.02012.0632.06.62.3524000
6ColorSam Raimi392.0156.00.04000.0James Franco24000.0336530303.0Action|Adventure|RomanceJ.K. SimmonsSpider-Man 338305646055Kirsten Dunst0.0sandman|spider man|symbiote|venom|villainhttp://www.imdb.com/title/tt0413300/?ref_=fn_tt_tt_11902.0EnglishUSAPG-13258000000.02007.011000.06.22.350
7ColorNathan Greno324.0100.015.0284.0Donna Murphy799.0200807262.0Adventure|Animation|Comedy|Family|Fantasy|Musical|RomanceBrad GarrettTangled2948102036M.C. Gainey1.017th century|based on fairy tale|disney|flower|towerhttp://www.imdb.com/title/tt0398286/?ref_=fn_tt_tt_1387.0EnglishUSAPG260000000.02010.0553.07.81.8529000
8ColorJoss Whedon635.0141.00.019000.0Robert Downey Jr.26000.0458991599.0Action|Adventure|Sci-FiChris HemsworthAvengers: Age of Ultron46266992000Scarlett Johansson4.0artificial intelligence|based on comic book|captain america|marvel cinematic universe|superherohttp://www.imdb.com/title/tt2395427/?ref_=fn_tt_tt_11117.0EnglishUSAPG-13250000000.02015.021000.07.52.35118000
9ColorDavid Yates375.0153.0282.010000.0Daniel Radcliffe25000.0301956980.0Adventure|Family|Fantasy|MysteryAlan RickmanHarry Potter and the Half-Blood Prince32179558753Rupert Grint3.0blood|book|love|potion|professorhttp://www.imdb.com/title/tt0417741/?ref_=fn_tt_tt_1973.0EnglishUKPG250000000.02009.011000.07.52.3510000
colordirector_namenum_critic_for_reviewsdurationdirector_fb_likesactor_3_fb_likesactor_2_nameactor_1_fb_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_fb_likesactor_3_namefacenumber_in_posterplot_keywordsmovie_imdb_linknum_user_for_reviewslanguagecountrycontent_ratingbudgettitle_yearactor_2_fb_likesimdb_scoreaspect_ratiomovie_fb_likes
5033ColorShane Carruth143.077.0291.08.0David Sullivan291.0424760.0Drama|Sci-Fi|ThrillerShane CarruthPrimer72639368Casey Gooden0.0changing the future|independent film|invention|nonlinear timeline|time travelhttp://www.imdb.com/title/tt0390384/?ref_=fn_tt_tt_1371.0EnglishUSAPG-137000.02004.045.07.01.8519000
5034ColorNeill Dela Llana35.080.00.00.0Edgar Tancangco0.070071.0ThrillerIan GamazonCavite5890Quynn Ton0.0jihad|mindanao|philippines|security guard|squatterhttp://www.imdb.com/title/tt0428303/?ref_=fn_tt_tt_135.0EnglishPhilippinesNot Rated7000.02005.00.06.3NaN74
5035ColorRobert Rodriguez56.081.00.06.0Peter Marquardt121.02040920.0Action|Crime|Drama|Romance|ThrillerCarlos GallardoEl Mariachi52055147Consuelo Gómez0.0assassin|death|guitar|gun|mariachihttp://www.imdb.com/title/tt0104815/?ref_=fn_tt_tt_1130.0SpanishUSAR7000.01992.020.06.91.370
5036ColorAnthony ValloneNaN84.02.02.0John Considine45.0NaNCrime|DramaRichard JewellThe Mongol King3693Sara Stepnicka0.0jewell|mongol|nostradamus|stepnicka|vallonehttp://www.imdb.com/title/tt0430371/?ref_=fn_tt_tt_11.0EnglishUSAPG-133250.02005.044.07.8NaN4
5037ColorEdward Burns14.095.00.0133.0Caitlin FitzGerald296.04584.0Comedy|DramaKerry BishéNewlyweds1338690Daniella Pineda1.0written and directed by cast memberhttp://www.imdb.com/title/tt1880418/?ref_=fn_tt_tt_114.0EnglishUSANot Rated9000.02011.0205.06.4NaN413
5038ColorScott Smith1.087.02.0318.0Daphne Zuniga637.0NaNComedy|DramaEric MabiusSigned Sealed Delivered6292283Crystal Lowe2.0fraud|postal worker|prison|theft|trialhttp://www.imdb.com/title/tt3000844/?ref_=fn_tt_tt_16.0EnglishCanadaNaNNaN2013.0470.07.7NaN84
5039ColorNaN43.043.0NaN319.0Valorie Curry841.0NaNCrime|Drama|Mystery|ThrillerNatalie ZeaThe Following738391753Sam Underwood1.0cult|fbi|hideout|prison escape|serial killerhttp://www.imdb.com/title/tt2071645/?ref_=fn_tt_tt_1359.0EnglishUSATV-14NaNNaN593.07.516.0032000
5040ColorBenjamin Roberds13.076.00.00.0Maxwell Moody0.0NaNDrama|Horror|ThrillerEva BoehnkeA Plague So Pleasant380David Chandler0.0NaNhttp://www.imdb.com/title/tt2107644/?ref_=fn_tt_tt_13.0EnglishUSANaN1400.02013.00.06.3NaN16
5041ColorDaniel Hsia14.0100.00.0489.0Daniel Henney946.010443.0Comedy|Drama|RomanceAlan RuckShanghai Calling12552386Eliza Coupe5.0NaNhttp://www.imdb.com/title/tt2070597/?ref_=fn_tt_tt_19.0EnglishUSAPG-13NaN2012.0719.06.32.35660
5042ColorJon Gunn43.090.016.016.0Brian Herzlinger86.085222.0DocumentaryJohn AugustMy Date with Drew4285163Jon Gunn0.0actress name in title|crush|date|four word title|video camerahttp://www.imdb.com/title/tt0378407/?ref_=fn_tt_tt_184.0EnglishUSAPG1100.02004.023.06.61.85456

Duplicate rows

Most frequently occurring

colordirector_namenum_critic_for_reviewsdurationdirector_fb_likesactor_3_fb_likesactor_2_nameactor_1_fb_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_fb_likesactor_3_namefacenumber_in_posterplot_keywordsmovie_imdb_linknum_user_for_reviewslanguagecountrycontent_ratingbudgettitle_yearactor_2_fb_likesimdb_scoreaspect_ratiomovie_fb_likes# duplicates
0Black and WhiteGeorge A. Romero284.096.00.056.0Duane Jones125.0236452.0Drama|Horror|MysteryJudith O'DeaNight of the Living Dead87978403S. William Hinzman5.0cemetery|farmhouse|radiation|running out of gas|zombiehttp://www.imdb.com/title/tt0063350/?ref_=fn_tt_tt_1580.0EnglishUSAUnrated114000.01968.0108.08.01.8502
1Black and WhiteYimou Zhang283.080.0611.0576.0Tony Chiu Wai Leung5000.084961.0Action|Adventure|HistoryJet LiHero1494146229Maggie Cheung4.0china|flying|king|palace|swordhttp://www.imdb.com/title/tt0299977/?ref_=fn_tt_tt_1841.0MandarinChinaPG-1331000000.02002.0643.07.92.3502
2ColorAlbert Hughes208.0122.0117.0140.0Jason Flemyng40000.031598308.0Horror|Mystery|ThrillerJohnny DeppFrom Hell12476541636Ian Richardson1.0freemason|jack the ripper|opium|prostitute|victorian erahttp://www.imdb.com/title/tt0120681/?ref_=fn_tt_tt_1541.0EnglishUSAR35000000.02001.01000.06.82.3502
3ColorAngelina Jolie Pitt322.0137.011000.0465.0Jack O'Connell769.0115603980.0Biography|Drama|Sport|WarFinn WittrockUnbroken1035892938Alex Russell0.0emaciation|male nudity|plane crash|prisoner of war|torturehttp://www.imdb.com/title/tt1809398/?ref_=fn_tt_tt_1351.0EnglishUSAPG-1365000000.02014.0698.07.22.35350002
4ColorBill Condon322.0115.0386.012000.0Kristen Stewart21000.0292298923.0Adventure|Drama|Fantasy|RomanceRobert PattinsonThe Twilight Saga: Breaking Dawn - Part 218539459177Taylor Lautner3.0battle|friend|super strength|vampire|visionhttp://www.imdb.com/title/tt1673434/?ref_=fn_tt_tt_1329.0EnglishUSAPG-13120000000.02012.017000.05.52.35650002
5ColorBrett Ratner245.0101.0420.0467.0Rufus Sewell12000.072660029.0Action|AdventureDwayne JohnsonHercules11568716235Ingrid Bolsø Berdal0.0army|greek mythology|hercules|king|mercenaryhttp://www.imdb.com/title/tt1267297/?ref_=fn_tt_tt_1269.0EnglishUSAPG-13100000000.02014.03000.06.02.35210002
6ColorBruce McCulloch52.085.054.0455.0Megan Mullally985.013973532.0Comedy|CrimeMartin StarrStealing Harvard112113065Chris Penn1.0black humor|crying during sex|harvard|humor|man with glasseshttp://www.imdb.com/title/tt0265808/?ref_=fn_tt_tt_192.0EnglishUSAPG-1325000000.02002.0637.05.11.852152
7ColorDan CurtisNaN99.045.0224.0Campbell Scott1000.0NaNFantasy|RomanceJennifer Jason LeighThe Love Letter14652166Estelle Parsons1.0antique|desk|letter|love|time travelhttp://www.imdb.com/title/tt0140340/?ref_=fn_tt_tt_156.0EnglishUSAUnratedNaN1998.0393.07.41.335152
8ColorDanny Boyle393.0101.00.0888.0Spencer Wilding3000.02319187.0Crime|Drama|Mystery|ThrillerRosario DawsonTrance926405056Tuppence Middleton0.0amnesia|criminal|heist|hypnotherapy|lost paintinghttp://www.imdb.com/title/tt1924429/?ref_=fn_tt_tt_1212.0EnglishUKR20000000.02013.01000.07.02.35230002
9ColorDavid Hewlett8.088.0686.0405.0David Hewlett847.0NaNComedyChristopher JudgeA Dog's Breakfast32622364Paul McGillion2.0dog|vegetarianhttp://www.imdb.com/title/tt0796314/?ref_=fn_tt_tt_146.0EnglishCanadaNaN120000.02007.0686.07.01.783772